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检索条件"任意字段=6th International Conference on the Art, Science, and Engineering of Programming, Programming 2022"

共 1074 条记录，以下是211-220 订阅

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Gaze Analysis and Modeling of Cognitive Process During Debugging for Novice Programmers’ Learning

Gaze Analysis and Modeling of Cognitive Process During Debug...

引用

thematic area Human Interface and the Management of Information, HIMI 2022 Part of the 24th HCI international conference, HCII 2022

作者： Yoshimori, Kohei Kano, Toru Akakura, Takako Graduate School of Engineering Department of Information and Computer Technology Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan Faculty of Engineering Department of Information and Computer Technology Tokyo University of Science 6-3-1 Niijuku Katsushika-ku Tokyo125-8585 Japan

ISBN: (纸本)9783031064234

Recently, the demand for IT human resources has increased. therefore, the expansion of programming education should be considered to strengthen the supply of it. this study aimed to propose debugging learning support for novice programmers using experts’ gaze behavior. First, we gave feedback to proficient users on their gazing behavior during program debugging and interviewed them about their cognitive processes. Based on the results, we developed a cognitive process model of expert debugging. However, most of the visualized information was based on gaze transitions, and it was difficult to see the characteristics of the cognitive process. this study helped to improve the previously proposed model and developed one that focuses more on the cognitive process during program debugging. Data from students with little programming experience were collected and compared with the cognitive process model of programming experts. this enabled the identification of the characteristics of the program debugging process of non-experts. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Program debugging

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Length-Constrained Mixed-Integer Convex programming-based Generation of Tensegrity Structures 6

Length-Constrained Mixed-Integer Convex Programming-based Ge...

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6th IEEE international conference on Advanced Robotics and Mechatronics (ICARM)

作者： Khafizov, Ramil Savin, Sergei Innopolis Univ Inst Robot Innopolis 420500 Russia

ISBN: (纸本)9780738133645

In this paper, a new method for generating tensegrity structures, based on mixed-integer programming and introduction of length constraints, is proposed. Tensegrity structures are of interest in a number of fields, and in Robotics they are expected to be used as structural elements of walking robots (tensegrity spines), as impact-resilient drones and as planetary exploration robots, among other things. the necessity for automated design tools for such structures makes it of interest to formulate tensegrity generation task as a mixed-integer convex program, making the process fast and reliable. Proposed length constraints allow to steer the solutions towards the desired shapes, such as tensegrity structures with smaller struts, hence easily foldable and compact. the study demonstrates three types of structures with different geometrical properties that we were able to generate with the proposed method, and discusses the growth in the time cost per problem with the increasing number of nodes in the structures.

关键词： Legged locomotion Bridges Shape Poles and towers programming Reliability engineering Space exploration

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Power Curve Estimation of a Wind Turbine Considering Different Weather Conditions using Machine Learning Algorithms 23

Power Curve Estimation of a Wind Turbine Considering Differe...

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23rd international Middle East Power Systems conference, MEPCON 2022

作者： Al-Gabalawy, Mostafa Ramadan, Haitham S. Mostafa, Mohammed A. Hussien, Shimaa A. El-Safawa High Institute of Engineering Electronics Communications Computers Engineering Department Cairo Egypt Zagazig University Faculty of Engineering Electrical Power and Machines Dept Zagazig Egypt Must University Faculty of Engineering Electrical Power and Machines Dept 6th October Cairo Egypt Pyramids Higher Institute for Engineering and Technology Electrical Power and Control Department 6th October Cairo Egypt

ISBN: (纸本)9781665463638

theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. there are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. this paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. there variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences;exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. the power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). the RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively. © 2022 IEEE.

关键词： Wind turbines

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A Hierarchical Optimization-Based Model for Microgrids with Energy Storage Considering Operation Cost and Net Load Mean Square Error 6

A Hierarchical Optimization-Based Model for Microgrids with ...

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6th IEEE conference on Energy Internet and Energy System Integration, EI2 2022

作者： Guan, Zexiang Cai, Dongsheng Bamisile, Olusola Li, Jian Zhang, Zhenyuan Huang, Qi Chengdu University of Technology School of Nuclear Technology and Automation Engineering Chengdu China University of Electronic Science and Technology of China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control Chengdu China

ISBN: (纸本)9798350347159

When a high proportion of renewable energy is connected to the grid, the net load fluctuation can be suppressed by configuring energy storage. However, the current cost of energy storage is high. this paper proposes a bi-level optimization model that takes into account the dynamic allocation of shared energy storage (SES) capacity in response to electric load demand. the upper-layer objective function adopts EO optimization algorithm, which is to maximize the income of the shared energy storage operator (SESO). the lower-layer multi-objective function is to minimize the sum of the operating cost and the net load mean square error of multiple microgrids, which can be classified as the mixed integer linear programming problem. then, the solution flow of the algorithm is given. Finally, under the constraints of the hierarchical optimization-based model, solving the problem by the CPLEX solver on the MATLAB platform. the results demonstrate that the proposed method can allocate optimal capacity and reduce operating costs for microgrids. Simultaneously, this method increases the revenue of SESO and promotes the consumption of renewable energy. © 2022 IEEE.

关键词： Mean square error

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A Two-Stage Structural Identification Method Using Jaya Algorithm and Gradient-Based Local Search 6th

A Two-Stage Structural Identification Method Using Jaya Algo...

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6th international conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures, SMAR 2022

作者： Zhang, Guangcai Xiong, Xiaobing Gao, Shuai Wan, Chunfeng Xue, Songtao Key Laboratory of Concrete and Pre-stressed Concrete Structure of Ministry of Education Southeast University Nanjing211189 China Research Institute of Structural Engineering and Disaster Reduction College of Civil Engineering Tongji University Shanghai200092 China Department of Architecture Tohoku Institute of Technology Miyagi Sendai982-8577 Japan

ISBN: (纸本)9789819933617

Numerous heuristic intelligence algorithms have been proposed to solve the optimization-based structural identification, while they suffer from slow computational efficiency and poor identification accuracy with predefined wide search space limits of unknown parameters. To deal with this issue, a two-stage structural identification method, initially implementing global search with Jaya algorithm and search space reduction (JSSR) method, subsequently identifying high-quality potential solution by implementing intensive local search starting from the previous identified best solution, is proposed and utilized in this paper. More specifically, in the former stage, Hammersley sequence sampling generates uniform initial population to preliminarily explore the whole solution space, and then the neighborhood of the global optimum is coarsely determined with Jaya algorithm in the reduced search range by search space reduction method. In the latter stage, the identified best solution is fine-tuned to the optimum with gradient-based sequential quadratic programming (SQP) method. the performance of genetic algorithm, butterfly optimization algorithm, improved butterfly optimization algorithm and proposed two-stage method (JSSR-SQP) are investigated by numerical case on a 16-element simply-supported beam structure for comparative study. the final identification results demonstrate that the proposed two-stage identification method can achieve much better identification accuracy with less computational resources than other three methods in the relatively large search space. © Tongji University Press 2024.

关键词： Computational efficiency

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Proceedings - 2019 6th international conference on Information science and Control engineering, ICISCE 2019

Proceedings - 2019 6th International Conference on Informati...

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6th international conference on Information science and Control engineering, ICISCE 2019

ISBN: (纸本)9781728157122

the proceedings contain 226 papers. the topics discussed include: towards just-in-time rational refactoring;leveraging developer discussions to make design accessible;effects of automated static analysis tools: a multidimensional view on quality evolution;SReYantra: automated software requirement inter-dependencies elicitation, analysis and learning;supporting the acquisition of programming skills with program construction patterns;detecting, understanding and resolving build and test conflicts;improving the software logging practices in DevOps;feedback in scrum: data-informed retrospectives;and stuck in the middle: removing obstacles to new program features through batch refactoring.

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Research on the Optimization of Rural Express Delivery Scheduling Based on the Truck-Mounted Drone Model

Research on the Optimization of Rural Express Delivery Sched...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE international conference on

作者： Haoli Qin Xiaojun Wang Xiqing Zhang Chunxia Yang College of Vehicle and Traffic Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Affected by terrain limitations, scattered demand, and other factors, the difficulty of rural express delivery has become an urgent problem for the logistics industry to solve. In response, a truck-mounted drone mobile station model has been proposed. Firstly, a mixed integer programming (MIP) model is established to minimize the total transportation cost as the optimization goal; secondly, the K-means clustering algorithm is used to determine the take-off and landing points for the drones, and a hybrid particle swarm optimization (PSO) algorithm is employed to find the transportation routes for the truck-mounted drone model; finally, in simulation experiments, the proposed truck-mounted drone mobile station model has reduced the cost by 20.09% and time by 13.26% compared to the traditional truck delivery model.

关键词： Integer programming Costs Job shop scheduling Clustering algorithms Transportation programming Particle swarm optimization Optimization Drones Logistics

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Analyse and Evaluate Quixbugs with Open AI Codex and Powering Next Generation Application

Analyse and Evaluate Quixbugs with Open AI Codex and Powerin...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Deepanshu Tyagi Sarvesh Tanwar Neetu Mittal Sumit Badotra Amity Institute of Information Technology Amity University Noida Uttar Pradesh India School of Computer Science and Engineering Bennett University Noida Uttar Pradesh India

the paper investigates the ordinary language age limits of massive language models, with applications to the development of two types of learning tools commonly found in programming courses. We construct programming workouts and code explanations, reviewing these abstractly and statistically, using the Open AI Codex as the massive language model. Our findings indicate that the majority of the content so given is both unique and sensible, and that it is occasionally ready to use without any guarantees. While rehearsing, we discovered that it is astonishingly simple to affect both the programming ideas and the practical subjects they include simply by offering expressions of commitment to the model. Findings indicate massively generative computer intelligence models have fundamental value.

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CoANE: Modeling Context Co-occurrence for Attributed Network Embedding 38

CoANE: Modeling Context Co-occurrence for Attributed Network...

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38th IEEE international conference on Data engineering (ICDE)

作者： Hsieh, I-Chung Li, Cheng-Te Natl Cheng Kung Univ Tainan Taiwan

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in embeddings. Existing ANE models do not consider the specific combination between graph structure and attributes. While each node has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, each node's neighborhood should be not only depicted by multi-hop nodes, but consider certain social circles. To model such information, in this paper, we propose a novel ANE model, Context Cooccurrence-aware Attributed Network Embedding (CoANE). the basic idea of CoANE is to model the context attributes for each node, and apply the convolutional mechanism to encode latent social circles. To better preserve network knowledge, we devise objective functions including positive graph likelihood, contextual negative sampling, and attribute reconstruction. We conduct experiments on five real datasets for downstream tasks, including node classification, link prediction, and community detection. the results exhibit that CoANE can significantly outperform state-ofthe-art ANE models. this work was accepted to IEEE TKDE

关键词： Knowledge engineering Correlation conferences Neural networks Linear programming Data engineering Task analysis

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Vehicle re-identification method based on semantic information enhancement and feature complementarity guided by keypoints 6

Vehicle re-identification method based on semantic informati...

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6th international conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Luo, Huicheng Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781510656635

Vehicle re-identification aims to retrieve and match vehicles under non-overlapping cameras. Although this technology has made some progress in recent years, the problem of vehicle appearance ambiguity caused by perspective shift still can not be well resolved. To alleviate the above problem, this paper proposes a vehicle re-identification method based on keypoint-guided semantic feature alignment and graph matching strategy to enhance complementary information under the transformer framework. the method first uses the pre-trained vehicle attitude detection model to extract keypoints, and proposes an information mapping strategy. through the coordinate information of keypoints, the corresponding local feature tokens under the transformer framework are extracted and given semantic content to achieve the same semantics. Feature alignment for attribute tokens. then, a graph matching network is constructed to realize the transfer of semantic information between similar samples, and the expressive ability of features is further improved through the multivariate interaction of information. the proposed model achieves state-of-the-art performance and superiority compared to similar methods on two large vehicle re-ID datasets. © 2022 SPIE.

关键词： Deep learning

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